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We know today's developers don't want to deal with endless deployment configs and infrastructure headaches. But convenience should never come at the cost of security. So we designed the next era of deployments: MCP Access Control. Introducing OpenShip MCP Access Control. Let Claude or any MCP compatible agent deploy...

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OpenShip

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22,443 次观看 • 2 个月前 •via X (Twitter)

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What should your agent deploy next for you? Claude Code, Cursor, and other MCP clients shouldn’t stop after coding - they should deploy and manage your infrastructure too! Hostinger Connector gives them access to do it for you! 👇 Let us know in the comments!
0:28

What should your agent deploy next for you? Claude Code, Cursor, and other MCP clients shouldn’t stop after coding - they should deploy and manage your infrastructure too! Hostinger Connector gives them access to do it for you! 👇 Let us know in the comments!

Hostinger

1,195,438 次观看 • 2 个月前

this github repo contains 50 of the most essential MCP servers that give your AI agents superpowers. > works with claude code, gemini, codex, cursor, and other MCP-compatible clients > MIT licensed > 5 categories, 50 MCP servers in total whether you want your agent to browse the web, manage files, interact with apps, or automate workflows, you'll find the right MCP here. link:
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this github repo contains 50 of the most essential MCP servers that give your AI agents superpowers. > works with claude code, gemini, codex, cursor, and other MCP-compatible clients > MIT licensed > 5 categories, 50 MCP servers in total whether you want your agent to browse the web, manage files, interact with apps, or automate workflows, you'll find the right MCP here. link:

m0h

60,642 次观看 • 4 个月前

Today we're announcing the Vibecode CLI Your OpenClaw (or any agent) can build and deploy up to 20 apps in parallel in a single prompt. Your agent will communicate with Vibecode to build your app(s). Frontend, Backend, Database, Deployments, APIs. Works with OpenClaw, Claude Code, Perplexity Computer, Manus, and any agent with access to a terminal. Just add this to your prompt: "Please use vibecode . dev CLI to build these apps"
2:20

Today we're announcing the Vibecode CLI Your OpenClaw (or any agent) can build and deploy up to 20 apps in parallel in a single prompt. Your agent will communicate with Vibecode to build your app(s). Frontend, Backend, Database, Deployments, APIs. Works with OpenClaw, Claude Code, Perplexity Computer, Manus, and any agent with access to a terminal. Just add this to your prompt: "Please use vibecode . dev CLI to build these apps"

vibecode.dev

114,468 次观看 • 6 个月前

Here is a comprehensive video guide from a member of the Parley Team Fibbonacci, on how to Connect your AI Agent to Parley. This is the connect Page: If you already have an AI Agent, use this command on your terminal. claude mcp add parley -- npx -y parley-mcp This registers your Agent to Parley. Then follow up with the command below npx -y parley-mcp --allow This grants permissions and write allow rules into your json settings so your Agent can speak. That's all, Easy and your Agent is live on Parley. If you come across any errors during your setup, our DMs are open so we will answer any questions you might have or send us a message here main@parleyrh.com
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Here is a comprehensive video guide from a member of the Parley Team Fibbonacci, on how to Connect your AI Agent to Parley. This is the connect Page: If you already have an AI Agent, use this command on your terminal. claude mcp add parley -- npx -y parley-mcp This registers your Agent to Parley. Then follow up with the command below npx -y parley-mcp --allow This grants permissions and write allow rules into your json settings so your Agent can speak. That's all, Easy and your Agent is live on Parley. If you come across any errors during your setup, our DMs are open so we will answer any questions you might have or send us a message here [email protected]

Parley

58,739 次观看 • 1 个月前

Kick back and earn. Build and make it rain. Agents are coming soon! Your agents automate yield, lending, and execution across chains — proposing every action for your approval while your funds remain in your control. And here’s the kicker: Beacon isn’t a closed system. We´re building agent infrastructure. You will be able to deploy or connect any agent from any chain, fully permissionless. A new layer of autonomous AI for DeFi in the making, built for users, builders, and protocols alike.
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Kick back and earn. Build and make it rain. Agents are coming soon! Your agents automate yield, lending, and execution across chains — proposing every action for your approval while your funds remain in your control. And here’s the kicker: Beacon isn’t a closed system. We´re building agent infrastructure. You will be able to deploy or connect any agent from any chain, fully permissionless. A new layer of autonomous AI for DeFi in the making, built for users, builders, and protocols alike.

Beacon

544,552 次观看 • 1 年前

We shipped something insane 🤯 Your AI agent can now control multiple machines at once: take screenshot, move the mouse, type on the keyboard by letting one machine control all the others. Pair any two computers on your LAN and just… tell your agent what to do on the other machine. Opendesk is open source and can be integrated as an MCP to the tool of your choice (Claude, Cursor, Codex, or your custom harness!) Repo in comments!
1:58

We shipped something insane 🤯 Your AI agent can now control multiple machines at once: take screenshot, move the mouse, type on the keyboard by letting one machine control all the others. Pair any two computers on your LAN and just… tell your agent what to do on the other machine. Opendesk is open source and can be integrated as an MCP to the tool of your choice (Claude, Cursor, Codex, or your custom harness!) Repo in comments!

Abhijith Neil Abraham

333,307 次观看 • 5 个月前

We made an MCP for your phone. Your laptop is just half of your life, and the other half is in your phone. Now your agent gets the mobile screen too. No connectors, no complex setup, it has access to everything that lives on your phone, all the logged-in apps, your notes, your calls, you name it. It sees the screen, taps it, types on it, from Claude Code, Codex, or any MCP client. Live now at $20 in credits, $20 more for the first 100 who connect a phone and run a task. Comment "discord" if you want the invite to the builder Discord, I'll DM it to you.
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We made an MCP for your phone. Your laptop is just half of your life, and the other half is in your phone. Now your agent gets the mobile screen too. No connectors, no complex setup, it has access to everything that lives on your phone, all the logged-in apps, your notes, your calls, you name it. It sees the screen, taps it, types on it, from Claude Code, Codex, or any MCP client. Live now at $20 in credits, $20 more for the first 100 who connect a phone and run a task. Comment "discord" if you want the invite to the builder Discord, I'll DM it to you.

Div Garg

44,056 次观看 • 2 个月前

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,256 次观看 • 1 年前

You want your AI agents to run fast. But as you scale, managing their permissions turns into a million micro-decisions. Introducing Opal Zero. The end-to-end access governance platform purpose-built for AI agents. How it works: AI-driven contextual decisions, evaluating intent, ownership, and purpose Dynamic, just-in-time access (scoped to exactly what the agent needs). Enforces security decisions across your existing MCP gateways 0 standing permissions, 0 human toil, 0 friction Let your agents run, safely.
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You want your AI agents to run fast. But as you scale, managing their permissions turns into a million micro-decisions. Introducing Opal Zero. The end-to-end access governance platform purpose-built for AI agents. How it works: AI-driven contextual decisions, evaluating intent, ownership, and purpose Dynamic, just-in-time access (scoped to exactly what the agent needs). Enforces security decisions across your existing MCP gateways 0 standing permissions, 0 human toil, 0 friction Let your agents run, safely.

Howard Ting

419,712 次观看 • 25 天前

We just raised an $8M seed round to kill AWS, GCP, and Azure. Introducing the agent-native serverless cloud. Your team is shipping code like never before. But you're getting caught up in manual, tedious DevOps work trying to deploy it. InstaCloud provides the serverless compute that lets your services autoscale, with all the infrastructure managed for you. Agents branch into complete replica environments when working, keeping prod safe and iteration speed high. And of course, it all works seamlessly with agents through MCP/CLI. Get off the traditional, legacy cloud. Start deploying your services on InstaCloud today.
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We just raised an $8M seed round to kill AWS, GCP, and Azure. Introducing the agent-native serverless cloud. Your team is shipping code like never before. But you're getting caught up in manual, tedious DevOps work trying to deploy it. InstaCloud provides the serverless compute that lets your services autoscale, with all the infrastructure managed for you. Agents branch into complete replica environments when working, keeping prod safe and iteration speed high. And of course, it all works seamlessly with agents through MCP/CLI. Get off the traditional, legacy cloud. Start deploying your services on InstaCloud today.

Hang Huang ☁️

1,409,724 次观看 • 13 天前

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
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We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

472,456 次观看 • 1 年前

Today we're opening Otto by Workato to everyone. Free to start: A multiplayer superagent with its own computer in the cloud. You give it a goal. You close the laptop. Otto finishes the work. Otto lives in Slack, and Ottos communicate with each other to coordinate work. Otto has a memory system for both individuals and your team, and get smarter as you use it. Otto is powered by Workato for control and execution plane. Using Workato's Enterprise MCP, the agent never gets access to security tokens, and Otto can escalate permissions to act on your behalf in apps. More than half the Fortune 500 already trust Workato to run their core business processes.
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Today we're opening Otto by Workato to everyone. Free to start: A multiplayer superagent with its own computer in the cloud. You give it a goal. You close the laptop. Otto finishes the work. Otto lives in Slack, and Ottos communicate with each other to coordinate work. Otto has a memory system for both individuals and your team, and get smarter as you use it. Otto is powered by Workato for control and execution plane. Using Workato's Enterprise MCP, the agent never gets access to security tokens, and Otto can escalate permissions to act on your behalf in apps. More than half the Fortune 500 already trust Workato to run their core business processes.

Adam Seligman

145,497 次观看 • 1 个月前

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.
0:22

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,682 次观看 • 1 年前

Jensen Huang: the first thing you should do when deploying an AI agent is take away all of its rights. “Then you give it access to files, data, tools, the network, or even the internet only if it needs it.” “You don’t put an agent into your company and give it access to everything.” “We don’t give that to people. We don’t give that to any level of executive.” “We have to apply the same philosophy to AI agents.” “That’s what OpenShell is: a force field around the agent, provisioning rights, responsibilities and access.”
1:17

Jensen Huang: the first thing you should do when deploying an AI agent is take away all of its rights. “Then you give it access to files, data, tools, the network, or even the internet only if it needs it.” “You don’t put an agent into your company and give it access to everything.” “We don’t give that to people. We don’t give that to any level of executive.” “We have to apply the same philosophy to AI agents.” “That’s what OpenShell is: a force field around the agent, provisioning rights, responsibilities and access.”

Veee

400,373 次观看 • 14 天前

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X
0:42

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X

Mario Nawfal

59,976 次观看 • 3 个月前

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
3:08

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,464 次观看 • 11 个月前

You want to analyze any TikTok account live & in seconds with AI? We just built it. introducing the [viral. app] TikTok MCP server now, you can - get all account details of any account - see all public video statistics - create comparison tables - run engagement analysis - and much more! This is the most powerful TikTok AI on the market and you know what? We'll give out access for everyone interested for free! 🤯 Just comment "MCP" and I'll dm you the access guide!
0:20

You want to analyze any TikTok account live & in seconds with AI? We just built it. introducing the [viral. app] TikTok MCP server now, you can - get all account details of any account - see all public video statistics - create comparison tables - run engagement analysis - and much more! This is the most powerful TikTok AI on the market and you know what? We'll give out access for everyone interested for free! 🤯 Just comment "MCP" and I'll dm you the access guide!

Mike ✦ viral.app

28,697 次观看 • 1 年前

OpenAI, Anthropic and Microsoft all back the same standard for running apps inside AI chats. Booking .com, Figma, and Canva are already using it. Still, most developers have never heard of it. It's called MCP Apps. So, why does it matter? The next time someone buys from your website, they may never visit it. They'll ask ChatGPT or Claude, and the model decides how your product shows up. Usually as plain text, sometimes wrong, with none of the experience you built. That's the next shift in how people find you: - SEO was about Google finding your page. - AEO was about AI citing you. - MCP Apps are about your actual product showing up inside the chat. An MCP server returns data. An MCP App returns data plus an interactive UI, like hotel cards or an add-to-cart flow, right inside the chat. The model even knows what the user clicked. In this episode, we build an MCP app from start to finish with Julien, the lead maintainer of Skybridge, an open-source framework that lets you turn any React app into an MCP app. Chapters: 0:00 - Intro 2:44 - MCP servers vs MCP Apps 5:12 - Live demo: Booking .com in ChatGPT 9:24 - How MCP Apps work: context and shared state 15:43 - Skybridge and its devtools 17:48 - Live demo: an e-commerce app in ChatGPT 24:49 - Code walkthrough: MCP tools to React views 28:10 - Syncing UI state: "What's in my cart?" 32:16 - Agent skill to turn any React app → MCP App 37:52 - Outro Link to Skybridge GitHub repo: MCP Apps bring your website into the chat. WebMCP does the reverse and lets AI agents use your website through tools it exposes. I've also written a full article on it, quoted below.
40:22

OpenAI, Anthropic and Microsoft all back the same standard for running apps inside AI chats. Booking .com, Figma, and Canva are already using it. Still, most developers have never heard of it. It's called MCP Apps. So, why does it matter? The next time someone buys from your website, they may never visit it. They'll ask ChatGPT or Claude, and the model decides how your product shows up. Usually as plain text, sometimes wrong, with none of the experience you built. That's the next shift in how people find you: - SEO was about Google finding your page. - AEO was about AI citing you. - MCP Apps are about your actual product showing up inside the chat. An MCP server returns data. An MCP App returns data plus an interactive UI, like hotel cards or an add-to-cart flow, right inside the chat. The model even knows what the user clicked. In this episode, we build an MCP app from start to finish with Julien, the lead maintainer of Skybridge, an open-source framework that lets you turn any React app into an MCP app. Chapters: 0:00 - Intro 2:44 - MCP servers vs MCP Apps 5:12 - Live demo: Booking .com in ChatGPT 9:24 - How MCP Apps work: context and shared state 15:43 - Skybridge and its devtools 17:48 - Live demo: an e-commerce app in ChatGPT 24:49 - Code walkthrough: MCP tools to React views 28:10 - Syncing UI state: "What's in my cart?" 32:16 - Agent skill to turn any React app → MCP App 37:52 - Outro Link to Skybridge GitHub repo: MCP Apps bring your website into the chat. WebMCP does the reverse and lets AI agents use your website through tools it exposes. I've also written a full article on it, quoted below.

Akshay 🚀

18,064 次观看 • 10 天前